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MICDE

MICDE Seminar: Jonathan Freund, University of Illinois at Urbana-Champaign

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Bio: Jonathan Freund is the Donald Biggar Willett Professor of Mechanical Science & Engineering and Aerospace at the University of Illinois at Urbana-Champaign.   He is a Fellow of the American Physical Society, and a winner of the 2008 Frenkiel Prize from its Division of Fluid Dynamics where he currently serves as the division secretary/treasurer.  He is an associate editor of Physical Review Fluids and on the editorial board of Annual Review of Fluid Mechanics.  Computational science has been central to his research, which has included simulations of turbulent jet noise and its control, the dynamics of molecularly thin liquid films, nanostructure formation by ion-bombardment of semiconductor materials, and most recently the dynamics of red blood cells flowing in the narrow confines of the microcirculation.  He co-directs the DOE-funded Center for Exascale Simulation of Plasma-Coupled Combustion at the University of Illinois.

Adjoint-based optimization for understanding and reducing flow noise

Advanced simulation tools, particularly large-eddy simulation techniques, are becoming capable of making quality predictions of jet noise for realistic nozzle geometries and at engineering relevant flow conditions.  Increasing computer resources will be a key factor in improving these predictions still further.  Quality prediction, however, is only a necessary condition for the use of such simulations in design optimization.  Predictions do not of themselves lead to quieter designs.  They must be interpreted or harnessed in some way that leads to design improvements.  As yet, such simulations have not yielded any simplifying principals that offer general design guidance. The turbulence mechanisms leading to jet noise remain poorly described in their complexity.  In this light, we have implemented and demonstrated an aeroacoustic adjoint-based optimization technique that automatically calculates gradients that point the direction in which to adjust controls in order to improve designs.  This is done with only a single flow solutions and a solution of an adjoint system, which is solved at computational cost comparable to that for the flow. Optimization requires iterations, but having the gradient information provided via the adjoint accelerates convergence in a manner that is insensitive to the number of parameters to be optimized.  The talk will review the formulation of the adjoint of the compressible flow equations for optimizing noise-reducing controls and present examples of its use.  We will particularly focus on some mechanisms of flow noise that have been revealed via this approach.

This seminar is co-sponsored by U-M Aerospace Engineering

MICDE Seminar: Jeremy Lichstein, University of Florida

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JeremyLichsteinBio: Jeremy Lichstein is an assistant professor of Biology at the University of Florida. Professor Lichstein got his Ph. D. from Princeton University and was a postdoctoral research fellow at Princeton’s department of Ecology and Evolutionary Biology. He was the recipient of the University of Florida Excellence Award for Assistant Professor, and was named a Florida Climate Institute Fellow for 2016-2017. His research interests are forest dynamics, biodiversity, carbon cycle and climate change.

Biodiversity and the changing Earth System: computational challenges and new answers to old questions

Terrestrial ecosystems currently offset roughly 25% of global annual anthropogenic fossil fuel emissions. However, the fate of this carbon sink is highly uncertain, in large part because global models diverge in their predictions of ecosystem responses to climate change, drought, and other perturbations. Although there is little agreement on how terrestrial ecosystems will respond to global change on decadal and longer time-scales, there is wide consensus that current global models are overly simplistic in their representation of important ecological processes. I will discuss our current understanding of how tree functional diversity is maintained in forests, the consequences of including more realistic levels of functional diversity in global models, and the computational challenges that need to be overcome in order to introduce ecological realism into the Earth System Models that the scientific and policy communities rely on for climate projections. A key result that is emerging from empirical and theoretical studies is that shifts in species composition across time or space (beta diversity) have different (and sometimes opposite) effects on ecosystem stability as local (alpha) diversity.

This seminar is co-sponsored by the U-M department of Ecology and Evolutionary Biology

MICDE Seminar: Rob Gardner, University of Chicago

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RobGardnerBio: Robert Gardner is a Senior Scientist at the Computation Institute from the University of Chicago,  and aSenior Scientist in the Enrico Fermi Institute. He spent his early academic career doing experimental high-energy physics research at different universities in the Midwest. He has been a member of the ATLAS experiment using the Large Hadron Collider at the CERN Laboratory, Geneva, Switzerland since 1998. His experimental work led him to specialize in developing and improving distributed computing technologies necessary for discoveries at the frontier of particle physics. He was instrumental in developing early research computing grids in the U.S.: the International Virtual Data Grid Laboratory (iVDGL), and the first deployment of the Open Science Grid (OSG) (NSF, Department of Energy). He have also generated systems for metrics collection for distributed systems (Grid Telemetry, PI, NSF-ITR). Currently, he directs the ATLAS Midwest Tier2 Center, which is comprised of integrated computing facilities from the University of Chicago, Indiana University, and the University of Illinois.

Leadership cyberinfrastructure for science and the humanities

In the past two decades high energy physics transformed its computing model from one relying on a single high performance computing center at the host laboratory to one incorporating resources distributed across institutional boundaries and geographic regions. Given the complexity of detectors and scale of data, the international collaborations of the Large Hadron Collider at CERN demanded it. By removing barriers to resource sharing, the resulting data and computation platform democratized the physics process across collaborations. Accelerated modes of scientific discovery by thousands of physicists were forged using hundreds of data centers linked by very high bandwidth networks. Meanwhile the explosion of commercial, social and enterprise data has driven innovation in resource abstraction and the creation of new service platforms, offering fresh opportunities to accelerate science and intellectual inquiry at all scales and across all domains. In this talk I’ll discuss the strategic significance that cyberinfrastructure technology plays in this regard and describe models for creating ubiquitous “substrates” that remove obstacles to connecting campuses, facilities, instruments and researchers.

This seminar is co-sponsored by the U-M department of Physics

Graduate Studies in Computational & Data Sciences Info Session – Central Campus

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2016-06-14 11.13.52Learn about graduate programs that will prepare you for success in computationally intensive fields — pizza and pop provided

  • The Ph.D. in Scientific Computing is open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”
  • The Graduate Certificate in Computational Discovery and Engineering trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan. This year we will offer a new practicum option through the Multidisciplinary Design Program.
  • The Graduate Certificate in Data Science is focused on developing core proficiencies in data analytics:
    1) Modeling — Understanding of core data science principles, assumptions and applications;
    2) Technology — Knowledge of basic protocols for data management, processing, computation, information extraction, and visualization;
    3) Practice — Hands-on experience with real data, modeling tools, and technology resources.

Graduate Studies in Computational & Data Sciences Info Session – North Campus

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2016-06-14 11.13.52Learn about graduate programs that will prepare you for success in computationally intensive fields — pizza and pop provided

  • The Ph.D. in Scientific Computing is open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”
  • The Graduate Certificate in Computational Discovery and Engineering trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan. This year we will offer a new practicum option through the Multidisciplinary Design Program.
  • The Graduate Certificate in Data Science is focused on developing core proficiencies in data analytics:
    1) Modeling — Understanding of core data science principles, assumptions and applications;
    2) Technology — Knowledge of basic protocols for data management, processing, computation, information extraction, and visualization;
    3) Practice — Hands-on experience with real data, modeling tools, and technology resources.

MICDE Seminar: Nathan Kutz, University of Washington

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Nathan.KutzBio: Nathan Kutz is the Robert Bolles and Yasuko Endo Professor in the department of Applied Mathematics, and an adjunct professor of Electrical Engineering and Physics at the University of Washington. He was awarded the B.S. in Physics and Mathematics from the University of Washington in 1990 and the PhD in Applied Mathematics from Northwestern University in 1994. Following postdoctoral fellowships at the Institute for Mathematics and its Applications (University of Minnesota, 1994-1995) and Princeton University (1995-1997), he joined the faculty of applied mathematics and served as Chair from 2007-2015.

Data-driven discovery of dynamical systems in the engineering, physical and biological sciences

We demonstrate that the integration of data-driven dynamical systems and machine learning strategies with adaptive control are capable of producing efficient and optimal self-tuning algorithms for many complex systems arising in the engineering, physical and biological sciences. We demonstrate that we can use emerging, large-scale time-series data from modern sensors to directly construct, in an adaptive manner, governing equations, even nonlinear dynamics, that best model the system measured using sparsity-promoting techniques. Recent innovations also allow for handling multi-scale physics phenomenon and control protocols in an adaptive and robust way. The overall architecture is equation-free in that the dynamics and control protocols are discovered directly from data acquired from sensors. The theory developed is demonstrated on a number of example problems. Ultimately, the method can be used to construct adaptive controllers which are capable of obtaining and maintaining optimal states while the machine learning and sparse sensing techniques characterize the system itself for rapid state identification and improved optimization.

This seminar is co-sponsored by the U-M Department of Mathematics.

Graduate programs in computational and data science — informational sessions Sept. 19 & 21

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Students interested in computational and data science are invited to learn about graduate programs that will prepare them for success in computationally intensive fields. Pizza and pop will be provided.

Two sessions are scheduled:

Monday, Sept. 19, 5 – 6 p.m.
Johnson Rooms, Lurie Engineering Center (North Campus)

Wednesday, Sept. 21, 5 – 6 p.m.
2001 LSA Building (Central Campus)

The sessions will address:

  • The Ph.D. in Scientific Computing, which is open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”
  • The Graduate Certificate in Computational Discovery and Engineering, which trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan. This year we will offer a new practicum option through the Multidisciplinary Design Program.
  • The Graduate Certificate in Data Science, which is focused on developing core proficiencies in data analytics:
    1) Modeling — Understanding of core data science principles, assumptions and applications;
    2) Technology — Knowledge of basic protocols for data management, processing, computation, information extraction, and visualization;
    3) Practice — Hands-on experience with real data, modeling tools, and technology resources.

MICDE Fall 2016 Seminar Series speakers announced

By | Educational, Events, General Interest, News

The Michigan Institute for Computational Discovery and Engineering (MICDE) is proud to announce its fall lineup of seminar speakers. In cooperation with academic departments across campus, the seminar series brings nationally recognized speakers to campus.

This fall’s speakers are:

Sept. 13: Nathan Kutz, Professor of Applied Mathematics, University of Washington

Sept. 22: Rob Gardner, Senior Scientist at the Computation Institute, University of Chicago

Sept. 29: Jeremy Lichstein, Assistant Professor of Biology, University of Florida

Oct. 6: Jonathan Freund, Professor of Mechanical Science and Engineering and of Aerospace Engineering, University of Illinois, Urbana-Champaign

Oct. 14: Anthony Wachs, Assistant Professor of Mathematics and of Chemical and Biological Engineering, University of British Columbia

Oct. 26: Andrea Lodi, Professor of Mathematical and Industrial Engineering, Polytechnique Montreal

Nov. 11: David Higdon, Professor of the Biocomplexity Institute, Virginia Tech

Dec. 9: Ann Almgren, Staff Scientist at the Center for Computational Sciences and Engineering, Lawrence Berkeley National Laboratories

For more information, including links to bios and abstracts as available, please visit micde.umich.edu/seminar-series/.

Students in the Graduate Certificate in Computational Discovery and Engineering program are required to attend at least half of the seminars.

New graduate course offering: “Methods and Practice of Scientific Computing”

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The Michigan Institute for Computational Discovery and Engineering (MICDE) is pleased to announce “Methods and Practice of Scientific Computing”, the first graduate course designed and organized by MICDE faculty. The course will be taught in Fall 2016, coordinated by Dr. Brendan Kochunas. This foundational course in scientific computing has been developed as a broad introduction to the subject, and has been designed to support research in all disciplines represented in MICDE. In addition to Brendan Kochunas, the course was developed by MICDE professors Bill Martin, Karthik Duraisamy, Vikram Gavini, and Shravan Veerapaneni, and MICDE Assistant Director Mariana Carrasco-Teja.

The details follow:

NERS 590
4 credits
Prerequisites: Graduate standing and permission of instructor.

This course is designed for graduate students who are developing the methods, and using the tools, of scientific computing in their research. With the increased power and availability of computers to do massively scaled simulations, computational science and engineering as a whole has become an integral part of research that complements experiment and theory. This course will teach students the necessary skills to be effective computational scientists and how to produce work that adheres to the scientific method. A broad range of topics will be covered including: software engineering best practices, computer architectures, computational performance, common algorithms in engineering, solvers, software libraries for scientific computing, uncertainty quantification, verification and validation, and how to use all the various tools to accomplish these things. The class will have lecture twice a week and have an accompanying lab component. Students will be graded on homeworks, lab assignments, and a course project.

A draft of the syllabus can be found here. Please contact MICDE at micde-contact@umich.edu with any questions.

Krishna Garikipati appointed Director of MICDE

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Statement from S. Jack Hu, U-M Vice President for Research:

krishnaGarikipatiI’m very pleased to announce that Prof. Krishna Garikipati (Mechanical Engineering and Mathematics) has been appointed the new Director of the Michigan Institute for Computational Discovery and Engineering (MICDE). The Institute has grown significantly since its establishment in 2013 as the interdisciplinary home for the development and use of mathematical algorithms on high performance computers at U-M. Prof. Garikipati has been involved as associate director for research since Fall 2014 and is uniquely positioned to take the institute to the next level.

MICDE is a joint initiative of UMOR, the College of Engineering, and the College of Literature, Science and the Arts. In the past year, it has seen many new and important developments, including the launching of two centers focused on network and storage-enabled collaborative science and data-driven computational physics; new planned course offerings for the PhD in Scientific Computing and the Graduate Certificate in CDE; new initiatives on industrial engagement; and the establishment of the Scientific Computing Student Club. A number of new research initiatives are also being planned, with broadening participation of MICDE-affiliated faculty, whose numbers continue to grow.

Prof. Garikipati will take over the directorship of MICDE from Prof. Eric Michielssen (EECS) who founded the institute in Fall 2013 and served as director, in addition to his role as Associate Vice President for Advanced Research Computing. Prof. Michielssen will continue as AVP.